The aim of the present work is to develop a ML potential for pure
aluminium dedicated to the description of condensed phases, namely
liquid and solid states for temperatures up to 8000 K and pressures
up to 300 GPa. For this purpose, we have developed a HDNN on the
basis of Behler and Parrinello's approach [1]}, [2]}.
The latter was trained on a data set generated by DFT-based simulations
for the main crystalline structures and liquid states covering the
targeted pressure and temperature domain, including their time
fluctuations by an appropriate sampling of phase space trajectories
obtained by AIMD. Training the HDNN solely on the basis of energies, the description
of the structure, dynamics and thermodynamics is shown to be accurate
in the investigated domain. The resulting potential is applied here
to solidification processes, namely amorphisation and/or early
stages of crystal nucleation, allowing us to unravel features of
the homogeneous nucleation mechanisms.
